Multimodal Aspect-Based Sentiment Analysis (MABSA) seeks to extract fine-grained information from image-text pairs to identify aspect terms and determine their sentiment polarity. However, existing approaches often fall short in simultaneously addressing three core challenges: Sentiment Cue Perception (SCP), Multimodal Information Misalignment (MIM), and Semantic Noise Elimination (SNE). To overcome these limitations, we propose DASCO (\textbf{D}ependency Structure \textbf{A}ugmented \textbf{Sco}ping Framework), a fine-grained scope-oriented framework that enhances aspect-level sentiment reasoning by leveraging dependency parsing trees. First, we designed a multi-task pretraining strategy for MABSA on our base model, combining aspect-oriented enhancement, image-text matching, and aspect-level sentiment-sensitive cognition. This improved the model's perception of aspect terms and sentiment cues while achieving effective image-text alignment, addressing key challenges like SCP and MIM. Furthermore, we incorporate dependency trees as syntactic branch combining with semantic branch, guiding the model to selectively attend to critical contextual elements within a target-specific scope while effectively filtering out irrelevant noise for addressing SNE problem. Extensive experiments on two benchmark datasets across three subtasks demonstrate that DASCO achieves state-of-the-art performance in MABSA, with notable gains in JMASA (+2.3\% F1 and +3.5\% precision on Twitter2015). The source code is available at https://github.com/LHaoooo/DASCO .

翻译：多模态方面级情感分析（MABSA）旨在从图文对中提取细粒度信息，以识别方面项并确定其情感极性。然而，现有方法往往难以同时应对三个核心挑战：情感线索感知（SCP）、多模态信息错位（MIM）和语义噪声消除（SNE）。为克服这些局限，我们提出了DASCO（依存结构增强的范围框架），这是一个细粒度的面向范围的框架，通过利用依存句法分析树来增强方面级情感推理。首先，我们在基础模型上为MABSA设计了一种多任务预训练策略，结合了面向方面的增强、图文匹配和方面级情感敏感认知。这提升了模型对方面项和情感线索的感知能力，同时实现了有效的图文对齐，解决了SCP和MIM等关键挑战。此外，我们引入依存树作为句法分支，与语义分支相结合，引导模型在目标特定范围内选择性地关注关键上下文元素，同时有效过滤无关噪声，以应对SNE问题。在两个基准数据集上针对三个子任务进行的广泛实验表明，DASCO在MABSA中实现了最先进的性能，在JMASA任务上取得了显著提升（Twitter2015数据集上F1值提升2.3%，精确率提升3.5%）。源代码发布于https://github.com/LHaoooo/DASCO。